Computers enable multiple uses of digital resources, which are often organized for convenient access by various indexing methods: for example, databases, document directories, structured document libraries, or full-text indexes. Very frequently, human computer users and information consuming programs have interest in combining digital resources from varying formats, collections, and indexes. There exists no general algorithm or automated method for combining diverse digital resources for arbitrary purposes.
For digital resources to be used in combination by automated processes, there must be indications about their properties: how they are used, under what conditions, etc. These indications can be located separately and independently from the resources themselves (e.g., as associated indices, databases, or documentation), or they may be directly associated with each resource. The latter resource indications are called metadata: data that refers to other data. Metadata, for instance, provide the ground-level basis for enabling the “semantic web”, currently being developed by the World Wide Web Consortium.
One example of current usage of metadata is in e-learning, as illustrated in FIG. 1. In this application as shown in FIG. 1, metadata are needed to provide structure for diverse types of resources. If educational resources can be associated with meaningful and reliable metadata, then they can be used as learning objects; that is, they can be combined into composite materials that can be used to satisfy educational goals. Generally stated: for digital educational resources to be effectively reused (arbitrarily accessed, recombined, applied in curricula), they must be associated with indications of their usage (prerequisites, grade level, assessment measures, etc.) which are typically expressed as metadata.
The pragmatic problem is how to “markup” the target resources with appropriate metadata. The current state of the art is to follow documented procedures for marking up metadata following some prescribed practices, possibly using some software tools to help guide and interactively document the process of metadata markup.
Although the overall process of using digital resources (i.e., creating, organizing, disseminating, finding, applying, and combining them) has heretofore required significant time and effort to be effective, many aspects have been accelerated and finely tuned: e.g., by full-text search, and by knowledge-management tools which aid in the task of organizing document collections. If documents have high-quality metadata, then even unstructured data (such as distributed educational resources from disparate sources) can be effectively used and reused in alternative ways. However, problems persist in getting high-quality metadata that are well associated with the resources that they are meant to describe. The main problems have been reliable tagging (consistent and reproducible methods rather than idiosyncratic), valid correspondence (stated relationships with other objects that fit with the views and perceptions of other computer users and of software tools), and evolution (reliable and valid changes of the metadata as other contextual matters change around the objects that they represent). This is still currently done by laborious processes, even when aided by commercial metadata-management software tools such as those offered by Autonomy Inc and Vivisimo Inc.
A related problem is that data standards such as “Dublin Core” (“DC,” http://dublincore.org) and “Metadata Object Description Schema” (“MODS,” maintained by the Library of Congress, http://www.loc.gov/standards/mods) emerge, and then change over time. Current metadata-management tools cannot adapt to multiple schema, and so different schema require different conventional tools. Also, conventional metadata markup is still too variable, difficult, and costly for people to manage efficiently and reliably. To be useful, software tools must fully automate the process, allowing people to select/modify/extend their requirements for metadata markup. Another problem is that validity of metadata will always degrade over time unless it can be automatically updated in response to changing conditions. Stated in a metaphorical way, people using Learning Objects, for example, should be enabled to act more like car drivers (selectors, connoisseurs, or managers), and less like mule drivers (overloaded and frustrated) or custom mechanics (experts in the technical innards of the objects).
Accordingly, there remains an acute and growing need in the art for an effective and completely automated process of metadata markup.